Abstract

Drone detection involves identifying the presence of unmanned aerial vehicles (UAVs) or drones in specific airspace. This technology has gained significance due to increased drone usage for various purposes, including civilian and military applications. As drone usage rises, concerns about potential risks like privacy infringement, malicious activities, and collisions with other aircraft are growing. Preventing unauthorized drone activities, such as espionage, smuggling, and terrorism, has become a critical security.Various methods, including radar, acoustic sensors, and video cameras, are employed in drone detection technology. These systems are integrated with software algorithms to accurately detect and track drones in real-time. This paper focuses on real-time drone detection using deep learning methods, specifically the VGG16 algorithm. Our experiments indicate that the VGG16 model offers improved accuracy and maintains a high detection speed.